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README.md
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path: re/validation-*
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- split: test
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path: re/test-*
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---
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path: re/validation-*
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- split: test
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path: re/test-*
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license: cc-by-4.0
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task_categories:
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- text-classification
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- token-classification
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language:
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- de
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tags:
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- finance
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- relation-extraction
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- event-extraction
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- traffic
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- industry
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pretty_name: SmartData Corpus
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size_categories:
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- 1K<n<10K
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---
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# Dataset Card for SmartData Corpus
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## Dataset Description
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- **Repository:** [https://github.com/dfki-nlp/smartdata-corpus](https://github.com/dfki-nlp/smartdata-corpus)
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- **Paper:** [A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events](https://www.dfki.de/web/forschung/projekte-publikationen/publikation/9427/)
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### Dataset Summary
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SmartData Corpus is a German-language dataset which is human-annotated with entity types and a set of 15 traffic- and
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industry-related n-ary relations and events, such as accidents, traffic jams, acquisitions, and strikes.
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The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and
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railway companies.
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This version of the dataset loader provides configurations for:
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- Named Entity Recognition (`ner`): NER tags use the `BIO` tagging scheme
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- Relation Extraction (`re`): n-ary Relation Extraction
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- Event Extraction (`ee`): formatted similar to https://github.com/nlpcl-lab/ace2005-preprocessing?tab=readme-ov-file#format
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For more details see https://github.com/dfki-nlp/smartdata-corpus and https://www.dfki.de/web/forschung/projekte-publikationen/publikation/9427/.
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### Supported Tasks and Leaderboards
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- **Tasks:** Named Entity Recognition, n-ary Relation Extraction, Event Extraction
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- **Leaderboards:**
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### Languages
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German
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## Dataset Structure
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### Data Instances
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#### ner
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An example of 'train' looks as follows.
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```json
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{
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"id": "671734738147758080",
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"tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
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"ner_tags": ["B-LOCATION_STREET", "O", "B-LOCATION", "I-LOCATION", "O", "B-LOCATION", "I-LOCATION", "O", "O", "O", "O", "O", "O", "B-TRIGGER", "O", "O", "O", "B-DATE", "O", "B-LOCATION_STREET"]
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}
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```
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#### re
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An example of 'train' looks as follows.
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```json
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{
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"id": "671734738147758080_0",
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"tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
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"entities": [[0, 1], [2, 4], [5, 7], [13, 14], [17, 18], [19, 20]],
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"entity_roles": ["location", "start_loc", "end_loc", "trigger", "end_date", "no_arg"],
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"entity_types": ["LOCATION_STREET", "LOCATION", "LOCATION", "TRIGGER", "DATE", "LOCATION_STREET"],
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"event_type": "Obstruction",
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"entity_ids": ["c/ac611f0a-d610-4ab2-9ddf-00132d9374b5", "c/3e01d530-58c4-4f47-9ab3-082a58e8299b", "c/cb6975e8-4409-4bdf-a491-de398b3c3263", "c/684a0ccd-06ff-4a8f-a90f-bdef169077dc", "c/166acddb-0f4d-48eb-98f6-a8b490f2e578", "c/ca3befa0-92da-4ff9-b34d-ec351854cdda"]
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}
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```
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#### ee
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An example of 'train' looks as follows.
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```json
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{
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"id": "671734738147758080",
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"text": "A1 Zwischen AS Munsbach und AS Flaxweiler Bauarbeiten, rechter Fahrstreifen gesperrt, Verkehrsbehinderung, Dauer: 02.12.2015... #ACL_A1\n",
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"entity_mentions": [
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{"id": "c/ac611f0a-d610-4ab2-9ddf-00132d9374b5", "text": "A1", "start": 0, "end": 1, "char_start": 0, "char_end": 2, "type": "LOCATION_STREET"},
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{"id": "c/3e01d530-58c4-4f47-9ab3-082a58e8299b", "text": "AS Munsbach", "start": 2, "end": 4, "char_start": 12, "char_end": 23, "type": "LOCATION"},
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{"id": "c/cb6975e8-4409-4bdf-a491-de398b3c3263", "text": "AS Flaxweiler", "start": 5, "end": 7, "char_start": 28, "char_end": 41, "type": "LOCATION"},
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{"id": "c/684a0ccd-06ff-4a8f-a90f-bdef169077dc", "text": "Verkehrsbehinderung", "start": 13, "end": 14, "char_start": 86, "char_end": 105, "type": "TRIGGER"},
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{"id": "c/166acddb-0f4d-48eb-98f6-a8b490f2e578", "text": "02.12.2015", "start": 17, "end": 18, "char_start": 114, "char_end": 124, "type": "DATE"},
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{"id": "c/ca3befa0-92da-4ff9-b34d-ec351854cdda", "text": "#ACL_A1", "start": 19, "end": 20, "char_start": 128, "char_end": 135, "type": "LOCATION_STREET"}
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],
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"event_mentions": [
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{
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"id": "r/802a82c2-c214-4429-b9f1-bf56e46674ee",
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"trigger": {
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"text": "Verkehrsbehinderung", "start": 13, "end": 14, "char_start": 86, "char_end": 105
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},
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"arguments": [
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{"text": "02.12.2015", "start": 17, "end": 18, "char_start": 114, "char_end": 124, "role": "end_date", "type": "date"},
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{"text": "AS Flaxweiler", "start": 5, "end": 7, "char_start": 28, "char_end": 41, "role": "end_loc", "type": "location"},
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{"text": "AS Munsbach", "start": 2, "end": 4, "char_start": 12, "char_end": 23, "role": "start_loc", "type": "location"},
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{"text": "A1", "start": 0, "end": 1, "char_start": 0, "char_end": 2, "role": "location", "type": "location-street"}
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],
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"event_type": "Obstruction"
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}
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],
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"tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
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"pos_tags": ["CARD", "APPR", "NE", "NE", "KON", "NE", "NE", "NN", "$,", "ADJA", "NN", "VVPP", "$,", "NN", "$,", "NN", "$.", "CARD", "$[", "CARD"],
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"lemma": ["a1", "zwischen", "as", "munsbach", "und", "as", "flaxweiler", "bauarbeiten", ",", "rechter", "fahrstreifen", "gesperrt", ",", "verkehrsbehinderung", ",", "dauer", ":", "02.12.2015", "...", "#acl_a1"],
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"ner_tags": ["B-LOCATION_STREET", "O", "B-LOCATION", "I-LOCATION", "O", "B-LOCATION", "I-LOCATION", "O", "O", "O", "O", "O", "O", "B-TRIGGER", "O", "O", "O", "B-DATE", "O", "B-LOCATION_STREET"]
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}
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```
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### Data Fields
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#### ner
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- `id`: example identifier, a `string` feature.
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- `tokens`: list of tokens, a `list` of `string` features.
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- `ner_tags`: list of NER tags, a `list` of `string` features.
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#### re
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- `id`: example identifier, a `string` feature.
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- `text`: example text, a `string` feature.
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- `tokens`: list of tokens, a `list` of `string` features.
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- `entities`: a list of token spans, a `list` of `int64` features.
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- `entity_roles`: a `list` of entity roles, a list of `string` features.
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- `event_type`: the event type, a `string` feature.
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- `entity_ids`: list of entity ids, a `list` of `string` features.
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#### ee
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- `id`: example identifier, a `string` feature.
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- `text`: example text, a `string` feature.
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- `entity_mentions`: a `list` of `struct` features.
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- `text`: a `string` feature.
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- `start`: token offset start, a `int64` feature.
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- `end`: token offset end, a `int64` feature.
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- `char_start`: character offset start, a `int64` feature.
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- `char_end`: character offset end, a `int64` feature.
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- `type`: entity type, a `string` feature.
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- `id`: entity id, a `string` feature.
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- `event_mentions`: a list of `struct` features.
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- `id`: event identifier, a `string` feature.
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- `trigger`: a `struct` feature.
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- `text`: a `string` feature.
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- `start`: token offset start, a `int64` feature.
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- `end`: token offset end, a `int64` feature.
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- `char_start`: character offset start, a `int64` feature.
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- `char_end`: character offset end, a `int64` feature.
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- `arguments`: a list of `struct` features.
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- `text`: a `string` feature.
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- `start`: token offset start, a `int64` feature.
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- `end`: token offset end, a `int64` feature.
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- `char_start`: character offset start, a `int64` feature.
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- `char_end`: character offset end, a `int64` feature.
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- `role`: role of the argument, a `string` feature.
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- `type`: entity type of the argument, a `string` feature.
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- `event_type`: a classification label, a `string` feature.
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- `tokens`: list of tokens, a `list` of `string` features.
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- `pos_tags`: list of part-of-speech tags, a `list` of `string` features.
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- `lemma`: list of lemmatized tokens, a `list` of `string` features.
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- `ner_tags`: a `list` of NER tags, a list of `string` features.
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### Licensing Information
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[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
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### Citation Information
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**BibTeX:**
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```
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@InProceedings{SCHIERSCH18.85,
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author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig},
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title = "{A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events}",
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booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
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year = {2018},
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month = {May 7-12, 2018},
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address = {Miyazaki, Japan},
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editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
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publisher = {European Language Resources Association (ELRA)},
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isbn = {979-10-95546-00-9},
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language = {english}
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}
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```
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**APA:**
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- Schiersch, M., Mironova, V., Schmitt, M., Thomas, P., Gabryszak, A., & Hennig, L. (2018). A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events. In N. Calzolari (Conference chair), K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, & T. Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. Unknown). Miyazaki, Japan: European Language Resources Association (ELRA). ISBN: 979-10-95546-00-9.
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### Contributions
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